MUSEBAQ: A Modular Tool for Music Research to Assess Musicianship, Musical Capacity, Music Preferences and Motivations for Music Use
Tan-Chyuan Chin1, Eduardo Coutinho2, Klaus R. Scherer3, and Nikki S. Rickard1, 4, 1Melbourne Graduate School of Education, The University of Melbourne, Australia
2Department of Music, University of Liverpool, United Kingdom 3Swiss Center for Affective Sciences, University of Geneva, Switzerland
4School of Psychological Sciences, Monash University, Australia
Corresponding author: Tan-Chyuan Chin
Melbourne Graduate School of Education The University Of Melbourne
Parkville, VIC 3010 Australia
Ph: 61-3-9035 8976
Email: tanchyuan.chin@unimelb.edu.au
Acknowledgements: The initial development of the Geneva Music Background Questionnaire (GEMUBAQ) which served as one input to the present work was supported by the Music and Emotion Focus of the Swiss Center for Affective Sciences. The authors also thank Raymond Macdonald for suggestions regarding musical identity items.
Abstract
The way people use and react to music is influenced by various determinants related to musicianship, musical capacities, music preferences and motivations for music use. This paper reports on the development of a multi-modular self-report instrument (the Music Use and Background Questionnaire, or MUSEBAQ) that measures these determinants in a consistent fashion. Based on earlier work, a hybrid approach of exploratory and confirmatory analyses was conducted across a series of three independent studies to establish reliability and validity of the modular tool. Module 1 (Musicianship) provides a brief assessment of formal and informal music knowledge and practice. Module 2 (Musical capacity) measures
emotional sensitivity to music, listening sophistication, music memory and imagery, and personal commitment to music. Module 3 (Music preferences) captures preferences from six broad genres and utilises adaptive reasoning to selectively expand sub-genres when
administered online. Module 4 (Motivations for music use) assesses musical transcendence, emotion regulation, social, and musical identity and expression. The MUSEBAQ offers researchers and practitioners a comprehensive, modular instrument that can be used in whole, or by module as required, to capture information related to listeners’ background and type of engagement with music, and to serve as a questionnaire to measureand interpret the effects of dispositional differences in emotional reactions to music.
Keywords: music engagement; musicianship; musical capacity; music preferences; motivations for music use.
The impact of music on cognitive and emotional functioning is increasingly of interest to researchers and practitioners (MacDonald, Kreutz, & Mitchell, 2012; Rickard & McFerran, 2012). It is widely accepted that the effects of music are moderated by an individual’s musical background and their level of engagement with music. For instance, researchers often distinguish between ‘musicians’ and ‘non-musicians’ in their samples, and music therapists are likely to tailor their therapies based on a patient’s music background. However, this distinction has often been limited to a gross measure of ‘musicianship’ – such as ‘years of formal music training’ – which fails to capture the myriad ways by which individuals engage actively with music. Several questionnaires have been developed which are designed to assess specific aspects of music engagement – such as music preferences (Rentfrow & Gosling, 2003), or use of music for mood regulation (Saarikallio, 2008). A
comprehensive, and psychometrically validated, instrument to assess the multidimensional nature of music engagement is however required to fully
acknowledge this construct in future research and practice in this field. Thus, in their Routes model of the determinants of music reactions Scherer and Coutinho (2013) suggested to minimally investigate Musical expertise, Stable dispositions and Current motivational / mood state as listener factors. This series of studies reports on the development and psychometric validation of a more extensive modular tool for measuring multiple dimensions of music engagement, including music background and capacity, music preferences, and motivations for using music in the general population.
Musicianship and Music Capacity
In its simplest form, musicianship is defined by categorizing individuals as ‘musicians’ or ‘non-musicians’. This dichotomy is useful in research which needs to broadly control for differences in music skill level, or the associated neurological differences (e.g., Merrett & Wilson, 2012). Musicianship is however a complex construct (see Rickard & Chin, 2016). Musicians are often further differentiated, for instance, by the frequency and duration (e.g., years) of their music training.
Musicians can also be self-taught, or acquire musical skill informally, as evidenced by the many prolific and highly skilled musicians who did not receive any formal
training (e.g., Frank Zappa, David Bowie, Django Reinhardt). ‘Non-musicians’ can also share many of the advanced skills of the trained musician, becoming highly adept at listening and interpreting music features (Bigand & Poulin-Charronnat, 2006; Lerdahl & Jackendoff, 1983). In this way, non-musicians can be ‘musical’ rather than ‘everyday’ listeners if they have sufficient knowledge and analytical music listening history to evaluate music in that way (Hargreaves, Hargreaves, & North, 2012). A non-performing listener of music can also be proficient with formal or informal music theory, despite no capacity for music practice.
Even without any music theory or practice skills, the majority of music listeners report emotional engagement with music, although this clearly varies across individuals. A recent study found that openness to aesthetics predicted musical sophistication in both musicians and non-musicians (Greenberg, Müllensiefen, Lamb, & Rentfrow, 2015). Empathetic individuals tend to respond emotionally to music, while individuals with a more systematizing personality tend to respond to more intellectually complex music (Greenberg, Baron-Cohen, Stillwell, Kosinski, & Rentfrow, 2015). Musicians are also more likely to respond analytically to music than affectively (e.g., Hargreaves & Colman, 1981). Music receivers can therefore
also be distinguished by their listening sophistication capacity, and their capacity to engage emotionally with music. Musicianship can perhaps therefore be better conceptualized as incorporating orthogonal dimensions of production and reception, with each dimension reflected on a continuum rather than a dichotomy (Chin & Rickard, 2012a).
Traditionally, music capacity is measured using a variety of auditory discrimination tasks (tones, chords/harmonic intervals, pitch, timbre, musical phrasing, rhythm etc.). There are also several behavioural batteries that measure a combination of music-related skills (Seashore Measures of Musical Talent, 1919; 1956; Kwalwasser-Dykema Music Test, 1930; The Wing Standardized Tests of Musical Intelligence, 1948). More recently, these perceptual musical skills are considered and conceptualized as individuals’ musical competence and can be assessed using the Profile of Music Perception Skills (PROMS; Law & Zentner, 2012). These aptitude tests primarily assess auditory perception and discrimination skills. However, the capacity to respond and understand music extends beyond such skills, and depends on an individual’s capacity to listen critically, to comprehend global music structures, and to appreciate both intellectual and affective intentions conveyed in music pieces. Reduced capacity for either cognitive or affective processing of auditory stimuli – as occurs in certain patients with localized
neurological lesions – significantly impairs appreciation of music (Gosselin, Peretz, Johnsen, & Adolphs, 2007; Peretz & Gagnon, 1999).
In the absence of a valid self-report measure of music capacity, however, it is challenging to study the impact of individuals’ sensitivity and capacity for listening, perceiving and understanding emotions conveyed in music. Despite their limitations, self-reports can capture an individual’s perception of how they perceive and respond to various types of emotion in music (for example, physiological responses such as
getting chills or gooseflesh, feelings of awe or amazement, experiencing strong emotions in response to particular types of music). The Barcelona Music Rewards Questionnaire (Mas-Herrero, Marco-Pallares, Lorenzo-Seva, Zatorre, & Rodriguez-Fornells, 2013) for example provides a self-report measure of an individual’s capacity to experience reward when listening to music. A self-report measure that recognizes an individual’s capacity for music listening and emotional sensitivity to music, in addition to their formal and informal musicianship, practical and theoretical music knowledge, would therefore significantly improve measurement of ‘musicianship’.
Music Preferences
Music preferences influence how individuals engage with music and overlap with musical identities and music listening habits (MacDonald, 2013; MacDonald et al., 2012). They remain important moderators to explore in research on the health outcomes of music, for instance, with the use of certain music preferences previously associated (not causally) with substance use, behavioural problems, and mood
regulation difficulties (Stack, Gundlack, & Reeves, 1994; Greenberg et al., 2015; North & Hargreaves, 2008; Garrido & Schubert, 2013; McFerran, Garrido, O’Grady, Grocke, & Sawyer, 2015; Miranda & Claes, 2009).
Music preferences are however challenging to measure. First, there is little agreement on what the basic genres should be, and it is recognized that these evolve over time (Rentfrow & Gosling, 2003). Second, broad classifications fail to recognize that passions can be quite finely localized to a sub-genre, so limiting respondents’ choices to the broad level lacks validity. Conversely, surveys that might aim to include an exhaustive list of sub-genres to date would very likely be unyielding and impractical. Third, instructions may fail to distinguish a listener’s ‘true preference’ for a type of music from their habitual behaviour (e.g., frequent listening due to
convenience or social group). An accurate measure of music preference should ensure the user’s choice is captured, but should also ideally distinguish between self-reported preferences that may be biased by experimenter demand or social identity desirability from those which are actually demonstrated by behavioural choices.
One method of overcoming the challenges of labelling and selecting music genres in a self-report survey is to obtain direct behavioural measures of people’s listening choices. A novel method of obtaining these data is via smartphone
technology, whereby ‘apps’ can automatically record listeners’ playlists as they occur in everyday life (see Randall & Rickard, 2013). This technology is still emerging however, and may confound listening practice (e.g., for convenience) with more deeply held preferences. Self-report measures therefore continue to be an important means of obtaining insight into a listener’s subjective preferences.
One of the most frequently used self-report measures of music preferences in the Short Test of Musical Preferences (the STOMP; Rentfrow & Gosling, 2003). Respondents rate their preference for 14 music genres (such as alternative, country, jazz or rock) on a 7-point scale (a revised version of the STOMP comprises 23 genres). Four music preference factors initially emerged from these data, but a five factor model has superseded this, identifying people’s preferences for mellow, unpretentious, sophisticated, intense or contemporary styles of music (‘MUSIC’ model; Rentfrow, Goldberg, & Levitin, 2011). These latent factors overcome the difficulties of labelling and limiting the number of music genres from which
respondents can choose, but are not meant to replace the STOMP. It is unlikely for instance, that respondents will easily identify with the factor labels ‘sophisticated’ or ‘unpretentious’. A flexible means of measuring preferences which allows both broad and finer level detail is therefore still needed to more effectively assess music
Music Use Motivations
One of the most enabling research findings relating to everyday use of music has been the elaboration of the various ways people use music in their lives. This understanding is shedding light into why both benefits and risk have been associated with music use in previous research. For instance, Chin and Rickard (2013, 2014) found that using music to regulation emotions or thoughts was associated with positive mental health well-being, while using music for social purposes was associated with poorer mental health. Any conceptualization of music engagement must therefore be capable of differentiating the primary motivations people have for using music.
There are numerous self-report questionnaires that tap into different reasons for using music. Several are quite targeted in their focus, for instance with the Music in Mood Regulation questionnaire (MMR; Saarikallio, 2008) testing various types of affective regulation with music, and the Barcelona Music Reward Questionnaire (BMRQ; Mas-Herrero et al., 2013) which assesses strongly hedonic or pleasurable experiences with music. Both these questionnaires have demonstrated replicability across studies, but are not intended to capture the broader spectrum of music use reasons.
Broader questionnaires which aim to assess a more comprehensive range of reasons for music use include the Uses of Music Inventory (UMI; Chamorro-
Premuzic & Furnham, 2007), the Music USE questionnaire (MUSE; Chin & Rickard, 2012b), the Music Use Inventory (MUI; Lonsdale & North, 2011) and the Brief Experiences with Music questionnaire (BMEQ; Werner, Swope, & Heide, 2006). Importantly, there is considerable overlap in the factors emerging from each of these instruments – for instance with affective functions, innovative/engaged production, identity functions and social functions emerging quite consistently. Nonetheless, each
of these questionnaires is limited in the psychometric data available. Importantly, these questionnaires each relied on university samples (mean ages around 20 years) for their development and testing. The MUSE was the only questionnaire initially developed from a primarily (88%) non-university sample (mean age 37.6 years), but it was then verified using a university sample. Each questionnaire was also tested on a relatively small sample size (around 300 participants). Finally, no reliability or validity psychometrics are reported for the UMI or MUI. Reliability is reported for the MUSE and BMEQ scales, but no validity is reported for any of these
questionnaires. There is therefore still a need for a psychometrically validated self-report questionnaire for measuring a broad range reasons for music use in a normative population.
A multidimensional modular instrument
This research demonstrates that it is crucial to obtain a broader picture of the ways in which individuals use music, and how a constellation of factors,
incorporating functions, processes, motivations of music engagement, sensitivity and personal commitment towards music, as well as preferences of music genre, needs to be measured and considered together. This series of studies aims to develop and establish reliability and validity of a modular tool for measuring the contributing aspects of music use, capacity and preferences to provide a comprehensive yet concise music engagement profiling tool for individuals.
Study 1 – Questionnaire Development
The aim of Study 1 was to generate items to assess the four dimensions of music engagement identified from past research: Musicianship, Musical Capacity, Music Preferences and Music Use Motivations. These items were subjected to scrutiny via focus groups style reviews and were revised in a reiterative manner until general satisfaction reached. The resulting questionnaire was then trialled and revised further subject to feedback.
Method
Five hundred and twenty-four undergraduate Psychology students (75% female); age range 18-57 (M=24.4, SD=6.6) participated in this study. The majority (81.3%) had English as their primary language. The sample had a mean of 3.38 years (SD: 5.88) of formal music theory training and 4.36 years (SD: 6.32) of formal practical music training.
Initial items were obtained from the MUSE (Chin & Rickard, 2012b), the GEMUBAQ (Coutinho & Scherer, 2014), and one item from the Goldsmiths Musical Sophistication Index (Müllensiefen, Gingras, Musil, & Steward, 2014). Additional items were generated by the authors in consultation with music researcher peers and Study 1 participants.
Participants reviewed the items in 16 separate focus group style class discussions, assessing the fit of each item to dimensions identified in the literature. The items were then refined on the basis of feedback and discussion, and the final questionnaire collated. This questionnaire was then administered in full to this sample. The completion time (trimmed mean) was 29 minutes.
Results and Discussion Module 1 (Musicianship)
Module 1 was created with items designed to measure formal and informal music knowledge and practice. To capture both quantity and quality of musicianship, items included years of training, frequency of practice, informal practice and a
subjective assessment of how much ‘do you know’. Items were also included to differentiate past training from current practice, and amateur/hobby music making. These questions were refined with feedback and reduced to a set of six items.
Module 2 (Musical capacity)
The second module about music capacity generated 31 items assessing both quantity and quality of music listening and general sensitivity to music. The majority of items were drawn from GEMUBAQ (Coutinho & Scherer, 2014), and one item from the Goldsmiths Musical Sophistication Index (Müllensiefen et al., 2014). Responses to item statements were added for the questionnaire using a 5-point Likert scale ranging from “1” (strongly disagree) to “5” (strongly agree).
Module 3 (Music preferences)
Using the STOMP-R as a starting point, focus groups were used to generate and refine a broad range of music genres for this module. Focus group participants were asked to describe different types of music, to try to group these into broader music type clusters, and label each cluster. The outcome of each focus group was compared, and the questionnaire genres were obtained from those for which there was strong agreement across groups, or by combining groups that were related by less strongly represented across groups.
Six broad genres emerged from focus group discussions; rock or metal; classical; pop or easy listening; jazz, blues, country or folk; rap or hip/hop; dance or electronica. Within each broad category, a range of subgenres was generated by focus group participants (see supplementary material 1 for a complete list of sub-genres). To minimise time demands on survey participants, the online administration of the survey utilised adaptive reasoning to selectively expand sub-genres based on prior selection from the six broad genres.
Participants were asked select which of the six main music genres would best fit their own first music preference. Over half the sample’s first preference was captured within two broad categories, ‘pop or easy listening’ and ‘rock or metal’, which reflects the relatively young population. They were also asked to indicate whether this classification was a “good” or “poor” fit; 84% of the sample confirmed their selection was a good fit for one of the six main categories. Broken down by genre (see Table 1), the best fits were reported for Dance or Electronica genre (Good: Poor fit - 16:1), classical genre (Good: Poor fit - 10:1), and pop or easy listening genre (Good: Poor fit - 7:1). The poorer fits were jazz, blues, country or folk (4:1) and rap or hip/hop (4:1), which may reflect the diverse collection of styles grouped into the former category, and the ongoing evolution of contemporary subgenres evolving in the latter category.
Table 1
Distribution of music preferences (1st preference) across sample and relative fit of
genre and sub-genre labels for participants’ preferred music.
Music preference (broad) Selected as 1st
preference Good Fit Not Good Fit 1. Rock or metal music N=115 (24%) 97 (84%) 18 (16%)
2. Classical music N=33 (7%) 30 (91%) 3 (9%)
3. Pop or easy listening music N=136 (28%) 118 (87%) 18 (13%)
4. Jazz, blues, country or folk music N=78 (16%) 61 (78%) 17 (22%)
5. Rap or Hip/Hop N=41 (9%) 32 (78%) 9 (22%) 6. Dance or Electronica N=48 (10%) 45 (94%) 3 (6%) 7. Other N=28 (6%) 20 (71%) 8 (29%) Excluding Other: N=479 N=451 403 (84%) 383 (80%) 76 (16%) 68 (14%)
For the questionnaire, the root stem, “How often do you choose to listen to any of the following styles of music?” was added. The wording of this question aimed to target the user’s deeper music preferences rather than habitual listening, but also recognized that strong preferences need to be reflected in behaviour. Responses to item statements were initially prepared as a 3-point ordinal scale; Never,
Sometimes, Often.
Module 4 (Music Use Motivations)
To develop a set of items which comprehensively assessed motivations for music use, focus group participants were prompted to generate as many reasons for listening to music as they could. Participants used the MUSE (Chin & Rickard, 2012b) as a starting point but were asked to critically reflect on the suitability and adequacy of these items for their own experience, and to develop items where gaps were perceived. This activity was offered in an undergraduate online class
the first session, and then reflect, discuss with family and peers, and refine their responses in the second session. They were also asked to group the suggestions into broader categories, and generate labels for each category.
This process generated a set of 57 items. Participants were asked to indicate whether their primary way of using music was captured in at least one of the
questionnaire’s items; 97% indicated agreement. Responses to item statements were added for the questionnaire using a 5-point Likert scale ranging from 1= strongly disagree to 5 = strongly agree.
Study 2 – Psychometric testing of questionnaire: Factor structure and reliability
The aim of Study 2 was to obtain a large normative data set for all four modules of the questionnaire generated in Study 1, and to explore the factor structure and reliability of Modules 1, 2 and 4. (Due to the adaptive reasoning presentation of Module 3, it was not possible to subject this module to such analyses.)
Method
Participants between the ages of 18 and 87 were recruited for Study 2 via convenience sampling in Victoria, Australia. Recruitment and online administration procedures in this study complied with the National Statement on Ethical Conduct in Human Research (2007), and were approved by the University Human Ethics
Committee. After agreeing to participate in this study, participants were provided with the survey link, where they provided informed consent and completed the online survey. Complete survey responses were obtained from 2964 individuals (40.4% male, 58.9% female, 0.7% unknown; AgeM = 32.0, AgeSD = 14.6). Study 2
participants were recruited by university students involved in Study 1, following guidelines encouraging recruitment of an equal proportion of males and females, and representation across a variety of musical experiences, age categories, and
socioeconomic strata. After agreeing to participate in this study, participants were provided with the survey link, where they provided informed consent and completed the online survey. Average completion time for the initial set of items was 37 minutes. All recruitment and procedures in this study complied with the National Statement on ethical conduct in human research (2007), and were approved by the University Human Ethics Committee.
Data screening and analyses were conducted using IBM SPSS Statistics version 22 (SPSSv22; IBM). Participants’ response timings were checked, as per guidelines recommended for web-based surveys (Reips, 2002). Additional checks were done to ensure that all variables were normally distributed, with no major concerns regarding multicollinearity.
A hybrid approach using both exploratory factor analysis (EFA) and
confirmatory factor analysis (CFA) was taken across Studies 2 and 3. This approach comprises three stages of analysis (Matsunaga, 2010):
1. screening and reducing items using principal component analysis (PCA) 2. determining the number of factors and identifying items which load onto
particular factors (EFA)
3. confirming the factor structure of the data (CFA) in Study 3
The sample of 2964 individuals was randomly split into two sub-samples to run PCA and EFA separately. The first sub-sample consisted of 1494 individuals (40.3% male, 59.1% female, 0.6% unknown) and the second with 1470 individuals (40.5% male, 58.8% female, 0.7% unknown). As Module 1 consistent of a small
number of items, factor analysis was limited to the first two stages only (PCA and EFA).
Results and Discussion
Module 1: Musicianship
Nearly 55% of participants indicated that they had no formal music theory training. The remaining participants had an average of 2.92 years of formal music theory training (range: 1 to 39 years). Approximately 47% of participants indicated that they had no formal practical music training. The remaining participants had an average of 4.07 years (range 1 to 60 year). The music background of participants on the other musicianship items is reported in Table 2. A factor analysis using PCA (promax rotation) of the six items for Module 1 revealed that items loaded on two dimensionsof musicianship, accounting for a total of 76.59% of variance. The first factor consisted of the first three items of the module, describing formal music training (accounting for 59.76% variance). The second factor comprised items
relating to more specifically to music making (accounting for an additional 16.84% of variance) (see Table 2). These two factors were moderately correlated, r = .53, N = 1457, p < .001, but orthogonal constructs, which indicates that this distinction could be useful in differentiating two distinct forms of musicianship. This sample
demonstrated significantly higher scores on the Music making factor (M = 10.48, SD = 3.00) than the Formal Music Training factor (M = 8.23, SD = 8.23), t (1456) = -8.39, p < .001. Internal reliability of each subscale was assessed and Cronbach’s α for the formal music training factor was .734, and for the music making factor was .814.
Table 2
PCA and EFA factor loadings of items in Module 1
Initial item code
Module 1 items Factor 1 Factor 2
PCA EFA PCA EFA MS1 Formal music training (theory) – years
.917 .924 MS2 Music structure and theory knowledge
.847 .839 MS3 Formal music training (practice) – years
.883 .902 MS4 Professional music making
.800 .785 MS5 Practice or rehearsal
.893 .889 MS6 Music making as a hobby/amateur
.870 .967 Factor 1: Formal music training; Factor 2: Music making.
A frequency analysis of the individual items was also performed as ‘years of training’ remains a useful comparative variable across studies (Table 3).
Table 3
Module 1 (Musicianship characteristics) of Study 2 participants
Item
Nothing A little A fair amount A moderate amount A great deal Knowledge about music structure and theory 923 (31.2%) (42.7%) 1262 (13.5%) 398 (8.5%) 250 (4.1%) 122
Never Rarely Sometimes Often All the time
Engage in professional music making 2183 (73.7%) (11.2%) 333 (7.8%) 231 (4.2%) 124 (3.1%) 93 Frequency of practice or rehearsal with an instrument or singing 1509 (50.9%) 564 (19.0%) 413 (13.9%) 323 (10.9%) 155 (5.2%) Engage in music making as a hobby or as an amateur 1468 (49.5%) (19.8%) 588 (14.7%) 436 (10.4%) 309 (5.5%) 163
These results highlight how Module 1 provides more detailed and useful information about the musicianship of participants that the traditional dichotomous
classification of ‘musician’ or the frequently used ‘years of music training’.
Traditional categorization of this sample as musicians and non-musicians would have identified between 12 and 15% of the sample as musicians based on professional status, or having at least a moderate level of music training. This module enables identification of a further 30% of the sample as non-professional music performers. This sample also has higher levels of practical music training (M=4.03 years, SD=3.25) than music theory (M=2.93 years, SD=5.00), t(2947)=-7.93, p<.001. Moreover, the sample has substantial informal music knowledge (69% know at least ‘a little’) and practice (30% practised at least ‘sometimes’), which would likely have been overlooked by classifying these individuals as ‘non-musicians’. Given this module was unidimensional, we recommend its use for contexts in which description of a sample beyond traditional ‘musician’ versus ‘non-musician’ would be
informative.
Module 2: Music capacity
For Module 2, PCA was first conducted using the sub-sample of 1494 participants in order to reduce the initial set of 31 items. The Kaiser-Meyer-Olkin (KMO) measure verified the sampling adequacy for the analysis, KMO = .94, and a significant Bartlett’s test of sphericity χ2 (465) = 20982.19, p < .001, indicated that
correlations between items were sufficiently large for factor analysis (Field, 2009). Promax rotation was used for all factor analyses. Items in each of the five displayed factors for this module were refined based on both theoretical and statistical
conditions aimed at increasing reliability and internal consistency of each factor. The following three criteria were set:
1) modulus item loadings were at least .40;
3) modulus item-total correlations were at least .40.
Using these criteria, 28 items were retained in the final solution (see Table 4 for factor loadings of items).
Table 4
PCA and EFA factor loadings of items in Module 2
Factor loadings of items
Initial item code Final item No.
Module 2 items Factor 1 Factor 2 Factor 3 Factor 4 Factor 5
PCA EFA PCA EFA PCA EFA PCA EFA PCA EFA MC13 MC10 Tears come to my eyes when
listening to some pieces of music .862 .653 MC28 MC2 I experience strong emotions when I
listen to particular types of music .766 .823 MC27 MC23 I can be greatly moved by music .765 .820 MC17 MC13 Music can produce feelings of
wonder and fascination in me .708 .718 MC12 MC9 I get chills or 'gooseflesh' when
listening to moving music .701 .686 MC16 MC6 I tend to appreciate music for its
beauty or sublimity .686 .613 MC29 MC24 Listening to music fills me with
emotion .670 .768
MC23 MC19 I sometimes seem to ‘catch’ the
emotions that I hear in the music .630 .673 MC22 MC18 When I listen to live music, I tend to
experience the emotions expressed
by the performers. .484 .551 MC14 MC15 I can’t help swaying my body or
tapping my foot when listening to
some music .469 .541
MC8 MC12 It’s important for me to choose each
piece of music I listen to .831 .554 MC9 MC16 It’s important that I give my full
attention to music when listening .788 .579 MC25 MC21 Music is like an addiction for me .702 .836 MC5 MC4 I often spend time online or in shops
looking for music .631 .595
MC26 MC22 I become so involved in music I’m listening to that I lose track of time or
where I am .625 .733
MC7 - I seek out live music listening
experiences .539 < .40
MC1 MC8 I couldn’t live without music .436 .594 MC19 MC3 I find it difficult to stop reliving my
past when I listen to some music .832 .699 MC20 MC7 I often see detailed pictures or
movies in my head when I listen to
music .790 .801
MC21 MC17 Images appear without any effort
when I hear music .739 .721
MC18 MC14 Music often evokes vivid memories
from my past .713 .668
MC2 MC1 After hearing a new song a few times,
I can usually sing or hum it by myself. .803 .646
MC3 MC20 I have a good ear for music .779 .734
MC6 MC5 I am able to describe a piece of music
I’ve heard to someone else .688 .721
MC4 MC11 I’m intrigued by music I’m not familiar with and want to find out
more .426 .579
MC30 MC25 I often feel bored while listening to
music -.825 -.537
MC10 MC26 I am quite indifferent to the presence
of music -.691 -.492
MC15 MC27 I never feel like dancing to music -.669 -.532 Note. Items retained after EFA are in bold font. Factor 1 label: Emotional music sensitivity; Factor 2
label: Personal commitment to music; Factor 3 label: Music memory and imagery; Factor 4 label: Listening sophistication; Factor 5 label: Indifference to music
After screening items using PCA above, EFA was then conducted using the responses from the second sub-sample of 1470 participants to determine the number of factors underlying the correlations among and variation in the shortlisted items, identify items that load strongly onto each of the extracted factors, and further reduce items that do not meet the criteria set previously. The KMO measure verified the sampling adequacy for the analysis, KMO = .93, and a significant Bartlett’s test of sphericity χ2 (378) = 18500.16, p < .001. Multicollinearity was also checked, with no
observed correlations above .70 among items. On the basis of Horn’s parallel
analysis (Thompson, 2004), the final factor (indifference to music) was not retained. These items are positioned at the end of this module’s administration to allow
researchers to easily omit them if only the most psychometrically robust factors are to be included. Should this factor be retained, researchers are advised to perform their own factor analysis to test its validity. The model without this factor explained 53.00% of the variance (see Table 5 for variance and sum of squared loading of each factor). The criteria used previously with PCA were also applied to this EFA, with the additional criterion of Cronbach’s alpha being greater than .70.
Table 5
Variance, Sum of Squared Loading and Alpha Reliability Coefficients of the Music Capacity Factors
Music capacity factors % of variance before rotation Rotation Sums of Squared Loadings Cronbach’s
Alpha Number of items retained
Emotional music sensitivity 34.38 7.94 .90 10
Listening sophistication 7.25 6.03 .77 4
Personal commitment to music 6.49 6.41 .81 6
Music memory and imagery 4.89 5.19 .81 4
Indifference to music 3.85 2.30 .59 (3)
Utilising both PCA and EFA, the exact same factor structure patterns were obtained across two independent samples, providing strong evidence in support of the obtained 4-factor solution.
Module 3: Music preferences
In this sample, the majority of participants (63.4%) reported often choosing to listen to pop/easy listening music. Preferences were fairly evenly distributed across other music genres. Preferences for sub-genres were complex, and many cells
contained low frequencies. Nevertheless, this module demonstrated the most popular sub-genres within each broader category (see Table 6).
Table 6
Module 3 (Music preferences) of Study 2 participants (nomination of genres and sub-genres not exclusive, so individuals can nominate more than one category)
Genre Never Sometimes Often Don’t Know Most popular
sub-genres listen to) (% often
Rock or Metal 764 (25.8%) 943 (41.6%) 23 (31.8%) 23 (0.8%) Alternative Classic Soft Indie
Rock and Roll
27.9 27.9 27.3 26.0 26.0 Classical 895 (30.2%) 1461 (49.3%) 581 (19.6%) 27 (0.9%) Instrumental Orchestral Classical 20th Century 22.4 17.3 19.3 14.8 Pop or Easy listening 176 (5.9%) 889 (30.0%) 1878 (63.4%) 21 (0.7%) Chart (top 40) Mainstream Oldies Easy listening 45.9 45.5 32.9 31.4 Jazz, blues,
country, folk (23.9%) 709 (44.9%) 1330 (29.9%) 887 (1.3%) 38 R&B Acoustic Blues Indie/ contemporary folk 20.2 18.9 15.3 15.3 Rap or
Hip/Hop (32.8%) 973 (38.8%) 1149 (27.1%) 802 (1.3%) 40 Hip/Hop Contemporary R&B Rap Urban 23.3 19.3 17.5 10.9 Dance or
Electronica (29.9%) 885 (40.1%) 1188 (28.4%) 841 (1.7%) 50 House Disco Electronic ambient Techno 18.3 10.4 10.3 10.2 Other 491 (16.6%) (35.3%) 1046 (26.9%) 796 (21.3%) 631 Musicals/ soundtracks World Religious Comedy 17.7 10.7 7.5 6.1
Intercorrelations between music preference categories were also explored (see Table 7). A preference for classical music was moderately correlated with a preference for jazz/blues/country/folk music, and a preference for rap or hip/hop music was
Table 7
Intercorrelations between Broad Music Preference Categories (N = 2964).
Classical
music Pop or easy listening music Jazz, blues, country or folk music Rap or
Hip/Hop Electronica Dance or Other
Rock or metal music .055 ** -.078** .130** .016 .003 .040* Classical music .020 .346** -.098** -.044* .075** Pop or easy listening music .104 ** .168** .139** .092** Jazz, blues, country or folk music .049** .011 .110** Rap or Hip/Hop .498** .153** Dance or Electronica .218** *p < .05; **p < .01
In this trial of the questionnaire, several subgenres were endorsed by very small numbers of respondents. To maintain as concise a questionnaire as possible, subgenres receiving less than 10% of the responses (e.g., breakbeat dance, zydeco, teen pop, mediaeval classical music, gothic rock, Christmas music) were removed from the final questionnaire. Feedback from respondents was also obtained and used to revise the final questionnaire. Participants indicated that the 3-point response items were not fine grained enough to allow them to provide the differentiation between genre preferences required. Responses were therefore revised to a 5-point Likert scale ranging from “1” (never) to “5” (always).
Music preference responses were not factor analysed as the aim of this
instrument was to generate the most usable data for music researchers or practitioners. The factor structure of music preference genres has previously explored (see
Rentfrow et al., 2011), but was considered less relevant to the aim of this paper which was to provide a useful questionnaire to assess music engagement in research and
practice. For this purpose, it is important to retain the terms used in everyday life to describe types of music as these are likely to be more easily interpreted by
participants and more relevant for researchers.
Module 4: Music Use Motivations
Similar to Module 2, a hybrid approach using PCA and EFA was taken for Module 4. The analyses were conducted using the same two sub-samples as per Module 2. PCA was first conducted using the sub-sample of 1494 participants in order to reduce the initial set of 57 items. The Kaiser-Meyer-Olkin (KMO) measure verified the sampling adequacy for the analysis, KMO = .98, and a significant
Bartlett’s test of sphericity χ2 (1596) = 55335.18, p < .001, indicated that correlations
between items were sufficiently large for factor analysis (Field, 2009). The same set of criteria used previously for Module 2 was also applied for Module 4. Based on the criteria, 41 items were retained after the initial PCA analysis (see Table 8 for factor loadings of items).
Table 8
PCA and EFA factor loadings of items in Module 4
Factor loadings of items Initial item code Final item No. Module 4 items
Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 PCA EFA PCA EFA PCA EFA PCA EFA PCA EFA PCA EFA
MM16 MM12 I like to use music for the very intense experience it gives me .852 .827 MM47 MM7 Music raises me to another state of mind .741 .735 MM34 MM16 Music exposes me to emotions I don’t often feel
.722 .585 MM35 MM25 Music helps me discover who I want to be .704 .727 MM2 MM1 I seek deep experiences through music .695 .780 MM22 MM18 Music inspires
new ideas and
thoughts in me .671 .728 MM31 MM23 Music listening sparks my creativity .603 .586 MM40 MM19 Music helps me understand who I am .596 .511 MM42 MM28 Music is like a comforting friend to me .519 .656
NA MM9 I feel that music
communicates what language can’t .447 .653 MM20 MM24 I use music to distract me from emotional pain .826 .849 MM26 MM20 I use music to help me work through my emotional problems .760 .733
MM23 MM27 I use music to get through difficult times .747 .727 MM30 MM22 I use music to explore and understand my own feelings .696 .725 MM17 MM13 I listen or play
music when I’m upset or feeling down
.684 .730
MM6 MM4 I like to use music to distract me
MM48 MM30 Playing music is an outlet for my anger or frustrations .614 .580 MM19 MM15 I use music to calm myself when I’m stressed or feeling anxious .592 .544 NA - I use music to help me reminisce or because it reminds me of the past .497 < .4 NA - I use particular pieces of music to improve my mood .468 < .4 NA - I use music to distract me from physical aches .412 .491 MM14 MM10 I like to listen to music that my friends like .899 .709 MM45 MM29 Music is more powerful when I experience it with others .596 .605 MM5 MM3 Having similar taste in music often helps me relate better to my peers .587 .497 MM3 MM2 Concerts often make me feel part of a community .568 .550
MM18 MM14 I often use music to feel a closer bond with other people .520 .553 MM15 MM11 Music is important for informing and maintaining relationships .433 .448 MM37 MM26 Music helps me feel comfortable around other people .414 .431 MM7 MM5 I consider myself a music 'fan' or music buff of certain types of music .774 .625 MM21 MM17 My music collection/playlist says a lot about me .753 .612 MM8 MM21 I dance, sing or play music to express my feelings .520 .631
MM11 MM8 I feel safe expressing my feelings through music .457 .614 NA - I imagine myself being like the performer or character in the music .423 < .4 NA MC31 Certain types of music help me think or concentrate .791 .473 NA MC32 I use music to
block out noise .722 .466
NA MC33 Music helps me to keep going on another task for a longer period of time .647 .568 NA - I use music to improve the atmosphere when I’m alone .472 < .4 NA - I use music to help me sleep .456 < .4 NA - I use background music to create a more pleasant space .442 < .4 NA - I exercise better with music .684 < .4 NA - I feel physically energized by music .599 < .4
Note. Items retained after EFA are in bold font. Factor 1: Musical transcendence; Factor 2: Emotional regulation; Factor 3: Social; Factor 4: Music identity and expression; Factor 5: Cognitive regulation;
After screening items using PCA above, EFA was then conducted using the responses from the second sub-sample of 1470 participants to determine the number of factors underlying the correlations among and variation in the shortlisted items, identify items that load strongly onto each of the extracted factors, and further reduce items that do not meet the criteria set previously. The KMO measure verified the sampling adequacy for the analysis, KMO = .97, and a significant Bartlett’s test of sphericity χ2 (820) = 36576.06, p < .001. Multicollinearity was also checked, with no
observed correlations above .70 among items. According to Horn’s parallel analysis (Thompson, 2004), four factors (with 30 items) should be retained, which in
combination explained 59.70% of the variance (see Table 9 for variance and sum of squared loading of each factor). Nonetheless, the fifth factor – cognitive regulation – was validated by PCA and EFA – and therefore may be retained by researchers, although factor analysis on their own data is recommended to confirm their validity. The items from this subscale are situated together at the end of this module’s
administration to allow researchers to easily omit them if only the most
psychometrically robust factors are to be included. The criteria used previously with PCA were also applied to this EFA, with the additional criterion of Cronbach’s alpha being greater than .70.
Table 9
Variance, Sum of Squared Loading and Alpha Reliability Coefficients of the Music Use Motivation Factors
Music motivation factors % of variance before rotation Rotation Sums of Squared Loadings Cronbach’s
Alpha Number of items retained
Musical transcendence 42.28 13.93 .92 10
Emotional regulation 5.31 13.91 .93 9
Social 4.07 8.97 .86 7
Music identity and expression 3.52 9.94 .79 4
Cognitive regulation 2.77 10.58 .69 (3)
Study 3 – Questionnaire Validation
A set of 67 items, grouped into four independent modules describing the various ways individuals engage with music emerged from Studies 2 and 3. As two existing questionnaires – the MUSE (Chin & Rickard, 2012b) and the GEMUBAQ (Coutinho & Scherer, 2014), contributed substantially to its creation, this
multidimensional instrument was named the ‘MUSEBAQ’ (or the Music USE and Background Questionnaire). This final study was designed to test the psychometric properties of the final MUSEBAQ on an independent community sample derived from Amazon Turk (a marketplace for recruiting user defined survey respondents). The aim of this study was to perform CFA on the multi-factor Modules 2 and 4, and test these subscales for concurrent validity using similar scales from existing
questionnaires.
Method Participants
Study 3 recruited a separate sample of 304 participants (51% male, 49% female) between the ages of 21 and 69 (AgeM = 35.13, AgeSD = 9.62) via Amazon
not had any formal music theory training. The remaining participants had an average of 3.56 years of formal music theory training (range: 1 to 26 years). As for formal music training (practice), 31% of participants reported that they have not had any training. The remaining majority of participants had 3.99 years of music training (range: 1 to 31 years). A summary of the distribution of music background experience of these participants is reported in Table 10.
Table 10
Demographics of Study 3 survey participants
Study 1 (N = 2964) Study 2 (N = 304)
Age Mean (SD) 32.03 (14.58) 35.13 (9.62)
Knowledge about music structure and theory
Nothing 923 (31.2%) 52 (17.1%)
A little 1262 (42.7%) 129 (42.4%)
A fair amount 398 (13.5%) 71 (23.4%)
A moderate amount 250 (8.5%) 36 (11.8%)
A great deal 122 (4.1%) 16 (5.3%)
Engage in professional music making
Never 2183 (73.7%) 105 (34.5%)
Rarely 333 (11.2%) 75 (24.7%)
Sometimes 231 (7.8%) 81 (26.6%)
Often 124 (4.2%) 27 (8.9%)
All the time 93 (3.1%) 16 (5.3%)
Frequency of practice or rehearsal with an instrument or singing
Never 1509 (50.9%) 75 (24.7%)
Rarely 564 (19.0%) 68 (22.4%)
Sometimes 413 (13.9%) 77 (25.3%)
Often 323 (10.9%) 69 (22.7%)
All the time 155 (5.2%) 15 (4.9%)
Engage in music making as a hobby or as an amateur
Never 1468 (49.5%) 75 (24.7%)
Rarely 588 (19.8%) 69 (22.7%)
Sometimes 436 (14.7%) 91 (29.9%)
Often 309 (10.4%) 52 (17.1%)
Materials
Formal and informal music knowledge and practice.
As with Study 1, the first module about music knowledge practice included six items to capture the formal music training and general music practice reported by the individual.
Musical capacity.
The second module about music capacity comprised 24 shortlisted items from Study 2 to assess both quantity and quality of music listening and general engagement with music. Responses to item statements were made on a 5-point Likert scale
ranging from 1=strongly disagree to 5=strongly agree.
Music preferences.
As with Study 1, the third module about music preferences included six broad genres with each broad category (rock or metal; classical; pop or easy listening; jazz, blues, country or folk; rap or hip/hop; dance or electronica) then filtered further down to sub-genres (see appendix for a complete list of sub-genres). Responses to item statements were made on a 5-point Likert scale ranging from “1” (never) to “5” (always).
Music Use Motivations.
Module four consists of 30 shortlisted items from Study 2 about why
individuals use music. As per Study 1, responses to item statements were made on a 5-point Likert scale ranging from “1” (strongly disagree) to “5” (strongly agree).
Battery of music scales for validity checks.
The following three scales were included to assess the validity of the modular MUSEBAQ measurement tool. The Uses of Music Inventory (UMI; Chamorro-Premuzic & Furnham, 2007) is a 15-item scale that measures three aspects of music use: Emotional; Cognitive; and Background. Responses to item statements were made on a 5-point Likert scale ranging from “1” (strongly disagree) to “5” (strongly agree), and reported Cronbach’s α range from .76 (Background subscale) to .85 (Cognitive subscale).
The Brief Music in Mood Regulation (B-MMR; Saarikallio, 2012) scale is a 21-item scale that measures mood regulation through music. Responses to item statements were made on a 5-point Likert scale ranging from “1” (strongly disagree) to “5” (strongly agree), and reported Cronbach’s α range from .73 (Diversion
subscale) to .85 (Solace subscale).
The Barcelona Music Reward Questionnaire (BMRQ; Mas-Herrero et al., 2013) is a 20-item, five-factor scale that measures facets of how individuals
experience reward associated with music. Responses to item statements were made on a 5-point Likert scale ranging from “1” (completely disagree) to “5” (completely agree), and reported Cronbach’s α range from .78 (Social Reward subscale) to .93 (Sensory Motor subscale).
The Music Use Inventory (Lonsdale & North, 2011) is a 31-item questionnaire asking participants to at how important music is in their lives, and then to rate 30 items on how well they described why they listen to music. In this study, 2 items only from the Identity subscale (“I listen to music to create an image for myself”, “I listen to music to portray a particular image to others”) were used to test the Identity subscale from Module 4, and to avoid repetition with other items from other
Likert scale ranging from “1” (not at all important) to “10” (extremely important”), and the Cronbach’s α for these two items from the current data was .71.
Procedure
All recruitment and online administration procedures in this study complied with the National Statement on ethical conduct in human research (2007), and were approved by the University Human Ethics Committee. After agreeing to participate in this study, participants were provided with the survey link, where they provided informed consent and completed the online survey. Once seven outliers (who completed the questionnaire over more than 24 hours) were removed, the mean time to complete the MUSEBAQ, Uses of Music Inventory, BMRQ, B-MMR and MUI was 14.86 minutes (SD: 11.80). The MUSEBAQ on its own (which is less than two thirds of the total battery) is estimated to take on average less than 10 minutes.
Data screening was conducted using IBM SPSS Statistics version 22
(SPSSv22; IBM) and CFA analyses were conducted with Mplus version 7.3 (Muthén & Muthén, 1998-2011). Model fit was evaluated primarily using the Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean Square Residual (SRMR). Both are population-based indices that are not affected by sample size (Hu & Bentler, 1999). In addition, both the Tucker-Lewis Index (TLI) and the Comparative Fit Index (CFI) are reported as additional metrics of model fit as recommended by Jackson and colleagues (2009). TLI and CFI relate to the total variance accounted for by a model, where values greater than .95 and .90 respectively are considered to indicate excellent and adequate fit to the data respectively (Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004). RMSEA and SRMR relate to the residual variance, where values smaller than .06 or .08 respectively indicate excellent and adequate model fit (Hu & Bentler, 1999; Kline, 2005).
Results and Discussion
Tovalidate the 4-factor structure obtained for both Modules 2 and 4 from the PCA and EFA in Study 2, CFA was conducted on a separate independent sample. Figure 1 shows the 4-factor model for Module 2 Music Capacity. Standardized estimates and errors are reported in the model.
Figure 1. 4-factor model for Module 2 Music Capacity. f1: Emotional music sensitivity; f2: Personal commitment to music; f3: Music memory and imagery; f4: Listening sophistication. Individual item codes are listed alongside module items in Table 3.
Model fit for a 4-factor structure for Module 2 Music Capacity was reasonably adequate, RMSEA = .07, SRMR = .06, TLI = .88, CFI = .90, as ascertained using Hu and Bentler’s (1999) benchmarks of RMSEA ≤ .06, SRMR ≤ .08, TLI ≥ .95, and CFI ≥ .95.
Figure 2 shows the 4-factor model for Module 4 Music Use Motivations. Standardized estimates and errors are reported in the model.
Figure 2. 4-factor model for Module 4 Music Use Motivations. f1: Music transcendence; f2: Emotion regulation; f3: Social; f4: Music identity and expression. Individual item codes are listed alongside module items in Table 7.
Model fit for a 4-factor structure for Module 4 Music Use Motivations was reasonably adequate, RMSEA = .07, SRMR = .05, TLI = .91, CFI = .91, as
ascertained using Hu and Bentler’s (1999) benchmarks of RMSEA ≤ .06, SRMR ≤ .08, TLI ≥ .95, and CFI ≥ .95.
Concurrent validity was tested via correlations between the MUSEBAQ Module 2 and 4 subscales with subscales from previous questionnaires testing similar constructs. Specifically, it was anticipated that for the MUSEBAQ Module 2: (a) Emotional music sensitivity subscale should correlate positively with the MMR Strong sensation and BMR Sensory Motor subscales; (b) Personal commitment to music subscale should correlate with the BMR Musical seeking subscale; (c) Listening sophistication should correlate with the UMI Cognitive subscale; and (d) Music memory and imagery should correlate with the MUI Identity subscale. Similarly, it was expected that the MUSEBAQ Module 4: (a) Music transcendence subscale should correlation positively with the UMI Cognition subscale; (b) Social subscale should correlate with the MRS Social rewards subscale; (c) Music identity and expression subscale should correlate with the musical seeking subscale; and (d) Emotion regulation subscale should correlate with the MRS Mood Regulation subscale and all the MMR subscales. These correlations are presented in Table 11, and confirm all expected correlations.
Table 11
Concurrent validity of MUSEBAQ subscales
MUSEBAQ Subscale Concurrent test subscale Correlation
Module 2 Emotional Sensitivity to music BMR Sensory Motor .56**
B-MMR Strong Sensation .71**
Module 2 Personal commitment BMR Musical Seeking .70**
Module 2 Listening sophistication UMI Cognitive .42**
Module 2 Music memory and imagery UMI Cognitive .30**
Module 4 Music transcendence UMI Cognitive .60**
Module 4 Music identity and expression MUI Identity .53**
Module 4 Social BMR Social Reward .81**
Module 4 Emotion regulation BMR Mood Regulation .74**
B-MMR Entertainment .62** B-MMR Revival .69** B-MMR Strong Sensation .69** B-MMR Diversion .77** B-MMR Discharge .50** B-MMR Mental Work .70** B-MMR Solace .76** **p < .01 General Discussion
The aim in this research was to develop an evidence-based flexible
questionnaire for assessing the broad range of ways in which individuals engage with music and to capture more comprehensively their musical background. The primary target audience for this questionnaire was music psychology researchers, although the questionnaire could also be useful for practitioners with an interest in tailoring their interventions on the basis of a client’s music engagement, or for more general researchers who are interested in the relationship of music engagement with another variable of interest. Theoretically driven items were generated to assess traditional and less formal musicianship, capacity to engage with music, music genre
preferences, and reasons or motivations for using music. Across a series of three independent studies, the items were subjected to a range of methodologies to reduce them to a robust set of factors that replicated across several samples. The resulting questionnaire – the MUSEBAQ – is a comprehensive, modular instrument that can be
used in whole, or by module as required. The entire profile provide substantially more information about an individual's musical engagement than has been previously available, and requires an average of less than 10 minutes to complete. The
individual modules can be used in isolation if a more targeted assessment is required, for instance of musical capacity.
Modules 1 and 3: Musicianship and preferences
Module 1 provides an overview of an individual’s music background with regard to both formal and informal music knowledge and practice. The two factors identified within this module are consistent with previous research which recognizes musicianship can exist without formal music training (Chin & Rickard, 2012a). These studies also demonstrated however that using six questions to detail an individual’s music background can be informative, and therefore researchers may choose to use each of these items to detail the musicianship demographics of their sample. Clearly this will provide a more comprehensive assessment of a participant’s musicianship than is traditionally achieved by ‘years of music training’ or identification as a professional ‘musician’. It may be misleading to suggest that the skills of listening and interpretation of music features are uniform within both categories of ‘musicians’ and ‘non-musicians’ (Bigand & Poulin-Charronnat, 2006, Hargreaves et al., 2012, Lerdahl & Jackendoff, 1983). Therefore, the use of Module 1 information will enable researchers to avoid blunt classifications that lose finer detailed information about an individual’s music knowledge or practice, which may be needed to shed light on differences amongst participant responses.
Module 3 provides a flexible means of obtaining detailed information on a sample’s music preferences. It achieves greater detail about sub-genres than previous questionnaires by using adaptive release reasoning, so that respondents are only
required to provide additional responses within only those genres to which they often or always listen. The subgenre labels will inevitably require updating in the future due to the rapid growth and differentiation of contemporary music types.
Nevertheless, the use of subgenre breakdown will enable participants to feel their selections are more authentic than when broad categories only are used. This was confirmed in the general agreement that for most genres, participants’ own music tastes were adequately captured by the labels provided. Furthermore, findings here of the links between preference for rock or metal music and classical, and
jazz/blues/county/folk music demonstrate support for past research that music preferences may also be driven by preferences for musical features or attributes, and not just music genre or type (Rentfrow et al., 2011). By using Module 3, in
combination with the other modules in this questionnaire, future studies can examine more broadly the relationships between music preferences with socio-psychological processes, environmental and emotional contexts, as well as musical capacity and motivations for engaging with music.
Modules 2 and 4: Musical capacity and motivations for use
Module 2 enables the individual variation in sensitivity or capacity to respond to music to be identified. This should be valuable in research where individual
differences in capacity to respond to music may explain differences in outcome measures of interest (e.g., an emotional response, or efficacy of a music medicine intervention). This study is the first to identify four robust factors within this
construct: emotional sensitivity; listening sophistication; music memory and imagery; and personal commitment to music. Each of these pathways reflect related but distinct ways in which individuals can become highly attuned to music, and thereby
may explain why some individuals respond more strongly than others to music exposure.
Module 4 captures the primary motivations underlying music use. Most notably, this is the first study to find ‘music transcendence’ to be the primary
motivation for music listening. Previous research has found that people tend to report using music for quite practical reasons which achieve greater happiness (emotion regulation), or connection to others (social, identity), or efficacy (background, physical). These uses were replicated here, with Emotion regulation, Social and Musical identity and expression confirmed as key motivations for using music. This study, however, is the first to yield a more spiritual or eudaimonic wellbeing factor in motivations for using music. The items contributing to the music transcendence scale tapped into the more intense, inspirational and otherworldly nature of music
experiences. Previous research has included items which also seem to measure this construct (e.g., the in the ‘Strong sensation’ subscale of the MMR (Saarikallio, 2008), or several items in the ‘Surveillance’ subscale of the MUI’ Lonsdale & North, 2011). It may be however, that insufficient items around this type of engagement with music in these questionnaires have meant that the items are subsumed and identified as part of other factors rather than a factor in its own right. This finding is consistent with qualitative accounts of music engagement which often depict strong experiences with music offering listeners new perspectives or insights into their lives, greater purpose or meaning in life, or a powerful spiritual experience (Gabrielsson, 2011). Inclusion of this factor brings psychometric testing of music engagement into better alignment with one of the most important, but to date omitted, reasons for using music.
The factor structure of Modules 2 and 4 were obtained via the gold standard, hybrid approach to factor analysis using PCA, EFA and CFA (Matsunaga, 2010). This allowed us to first identify the set of latent factors that capture both musical
capacity and music use motivation, and subsequently test the underlying structure of both constructs and investigate if the models fit adequately. Results across the three stages of factor analyses provide strong support for the underlying structure of the two modules. In Study 2, a large sample was randomly subdivided into two sub-samples. Factor loadings of retained items and factor structure were consistent across both sub-samples. Furthermore, shortlisted items from study 2 were used in the CFA in study 3, utilising another independent sample, with a combination of fit indices reflecting an adequate model fit. The consistency of results across different samples provides additional support for the 4-factor structure. This process also uncovered several factors that were not sufficiently robust to be retained. While theoretically supported, ‘Indifference to music’ (Module 2), and ‘Physical’, ‘Cognitive regulation’
motivations (Module 4) were excluded following CFA, although the validity of the latter factor is supported partially and therefore can be retained with caution.
The psychometric properties of the MUSEBAQ were also strong. Internal reliability (Cronbach’s alphas) for all Module 2 and 4 subscales was strong (ranging from .77 to .93), and concurrent validity with previous measures of music engagement and use was demonstrated. While other forms of validity will be tested in future research, Study 3 suggested that there may limitations in the MUSEBAQ’s discriminant validity. The majority of subscales and factors tested correlated positively and strongly with each other. While not entirely surprising given that the same individual is likely to exceed or lack a range of related music habits, previous research has indicated that engaging with music in certain ways can be predictive of quite different outcomes (Chin & Rickard, 2013, 2014). Further research (e.g., using cluster analysis) on data from the MUSEBAQ will be important to determine how individuals are differentiated in their music use patterns.
The MUSEBAQ is a relatively brief, flexible and comprehensive questionnaire which has demonstrated psychometric validity and reliability. Its modular nature allows it to be fit to purpose, and reduces demand on participants. Indices can be obtained for a range of sub-scales enabling insight into the quality of music engagement rather than only the frequency. While further research is required to determine whether a global ‘music engagement’ index is meaningful, the current MUSEBAQ generates rich data on four distinct aspects of music engagement (see Figure 3). Researchers and practitioners are encouraged to use this instrument to obtain rich data about their participants’ music background and uses, and to allow a more consistent comparability of samples across studies. This is particularly
important for divergent findings or failure to replicate in cases in which listener samples diverge with respect to musical engagement and background factors that may have affected reactions to different kinds of music. Obviously, the empirical
assessment and specification of the probability of such effects constitute an important agenda for research in its own right.
Figure 3. MUSEBAQ modules and component subscales. (Shaded subscales were supported by PCA and EFA, but not CFA so are to be retained with caution).
Module 1 Musicianship Formal and informal music knowledge and practice Module 2 Musical capacity Listening sophistication Emotional sensitivity Music memory and imagery Personal commitment Indifference to music Module 3 Music preferences Rock/metal Classical Pop/ Easy Listening Jazz, Blues, Country, Folk Rap /Hip-Hop Dance/ Electronica Module 4 Motivations for music use
Musical transcendence Emotion regulation Social Musical identity and expression Cognitive regulation
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